import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel





class UpperCAmelCase		:


				'''simple docstring'''






				def __init__(		self						,      lowercase						,      lowercase=9_9						,      lowercase=1_3						,      lowercase=7						,      lowercase=9						,      lowercase=True						,      lowercase=True						,      lowercase=False						,      lowercase=3_2						,      lowercase=5						,      lowercase=4						,      lowercase=3_7						,      lowercase=8						,      lowercase=0.1						,      lowercase=0.002						,      lowercase=1						,      lowercase=0						,      lowercase=0						,      lowercase=None						,      lowercase=None						,      ):



						"""simple docstring"""





						A_						:							List[Any]      = parent
						A_						:							Optional[int]      = batch_size
						A_						:							str      = encoder_seq_length
						A_						:							Tuple      = decoder_seq_length
						# For common tests
						A_						:							str      = self.decoder_seq_length
						A_						:							Union[str, Any]      = is_training
						A_						:							str      = use_attention_mask
						A_						:							Union[str, Any]      = use_labels
						A_						:							Tuple      = vocab_size
						A_						:							Optional[int]      = hidden_size
						A_						:							Union[str, Any]      = num_hidden_layers
						A_						:							Any      = num_attention_heads
						A_						:							Optional[int]      = d_ff
						A_						:							Optional[Any]      = relative_attention_num_buckets
						A_						:							str      = dropout_rate
						A_						:							Optional[Any]      = initializer_factor
						A_						:							List[Any]      = eos_token_id
						A_						:							int      = pad_token_id
						A_						:							Optional[int]      = decoder_start_token_id
						A_						:							Any      = None
						A_						:							List[str]      = decoder_layers
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						return TaConfig.from_pretrained('google/umt5-base'   )
				def     lowerCAmelCase_  (		self						,      lowercase						,      lowercase						,      lowercase						,      lowercase=None						,      lowercase=None						,      lowercase=None						,      lowercase=None						,      lowercase=None						,      ):



						"""simple docstring"""





						if attention_mask is None:
								A_						:							Optional[int]      = input_ids.ne(config.pad_token_id   )
						if decoder_attention_mask is None:
								A_						:							Dict      = decoder_input_ids.ne(config.pad_token_id   )
						if head_mask is None:
								A_						:							Optional[Any]      = torch.ones(config.num_hidden_layers						,      config.num_attention_heads						,      device=lowercase   )
						if decoder_head_mask is None:
								A_						:							List[Any]      = torch.ones(config.num_decoder_layers						,      config.num_attention_heads						,      device=lowercase   )
						if cross_attn_head_mask is None:
								A_						:							Any      = torch.ones(
								    config.num_decoder_layers						,      config.num_attention_heads						,      device=lowercase   )
						return {
						    "input_ids": input_ids,
						    "decoder_input_ids": decoder_input_ids,
						    "attention_mask": attention_mask,
						    "decoder_attention_mask": decoder_attention_mask,
						    "head_mask": head_mask,
						    "decoder_head_mask": decoder_head_mask,
						    "cross_attn_head_mask": cross_attn_head_mask,
						}
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						A_						:							str      = ids_tensor([self.batch_size, self.encoder_seq_length]						,      self.vocab_size   )
						A_						:							Optional[Any]      = ids_tensor([self.batch_size, self.decoder_seq_length]						,      self.vocab_size   )

						# we need to clamp the input ids here to avoid having pad token in between
						# this is because for NllbMoe the position_ids are prepared such that
						# all pad tokens have pos id = 2 and rest are between 2..seq_length
						# and the seq_length here is seq_length - num_pad_tokens
						# but when using past, there is no way of knowing if the past input ids had
						# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
						# position_ids being off by num_pad_tokens in past input
						A_						:							Any      = input_ids.clamp(self.pad_token_id + 1   )
						A_						:							List[str]      = decoder_input_ids.clamp(self.pad_token_id + 1   )

						A_						:							Union[str, Any]      = self.get_config()
						A_						:							Tuple      = config.num_attention_heads
						A_						:							List[Any]      = self.prepare_inputs_dict(lowercase						,      lowercase						,      lowercase   )
						return config, input_dict
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						A_  ,     A_						:							Dict      = self.prepare_config_and_inputs()
						return config, inputs_dict
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						return TaConfig(
						    vocab_size=1_6_6						,      d_model=self.hidden_size						,      d_ff=self.d_ff						,      d_kv=self.hidden_size // self.num_attention_heads						,      num_layers=self.num_hidden_layers						,      num_decoder_layers=self.decoder_layers						,      num_heads=self.num_attention_heads						,      relative_attention_num_buckets=self.relative_attention_num_buckets						,      dropout_rate=self.dropout_rate						,      initializer_factor=self.initializer_factor						,      eos_token_id=self.eos_token_id						,      bos_token_id=self.pad_token_id						,      pad_token_id=self.pad_token_id						,      decoder_start_token_id=self.decoder_start_token_id						,      )
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						return TaConfig(
						    vocab_size=self.vocab_size						,      d_model=self.hidden_size						,      d_ff=self.d_ff						,      d_kv=self.hidden_size // self.num_attention_heads						,      num_layers=self.num_hidden_layers						,      num_decoder_layers=self.decoder_layers						,      num_heads=self.num_attention_heads						,      relative_attention_num_buckets=self.relative_attention_num_buckets						,      dropout_rate=self.dropout_rate						,      initializer_factor=self.initializer_factor						,      eos_token_id=self.eos_token_id						,      bos_token_id=self.pad_token_id						,      pad_token_id=self.pad_token_id						,      decoder_start_token_id=self.decoder_start_token_id						,      )
				def     lowerCAmelCase_  (		self						,      lowercase						,      lowercase						,      lowercase						,      lowercase						,      lowercase						,      lowercase						,      ):



						"""simple docstring"""





						A_						:							List[str]      = UMTaModel(config=lowercase   )
						model.to(lowercase   )
						model.eval()
						A_						:							List[Any]      = model(
						    input_ids=lowercase						,      decoder_input_ids=lowercase						,      attention_mask=lowercase						,      decoder_attention_mask=lowercase						,      )
						A_						:							Tuple      = model(input_ids=lowercase						,      decoder_input_ids=lowercase   )
						A_						:							List[Any]      = result.last_hidden_state
						A_						:							List[str]      = result.past_key_values
						A_						:							Union[str, Any]      = result.encoder_last_hidden_state

						self.parent.assertEqual(encoder_output.size()						,      (self.batch_size, self.encoder_seq_length, self.hidden_size)   )
						self.parent.assertEqual(decoder_output.size()						,      (self.batch_size, self.decoder_seq_length, self.hidden_size)   )
						# There should be `num_layers` key value embeddings stored in decoder_past
						self.parent.assertEqual(len(lowercase   )						,      config.num_layers   )
						# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
						self.parent.assertEqual(len(decoder_past[0]   )						,      4   )
				def     lowerCAmelCase_  (		self						,      lowercase						,      lowercase						,      lowercase						,      lowercase						,      lowercase						,      lowercase						,      ):



						"""simple docstring"""





						A_						:							Any      = UMTaModel(config=lowercase   ).get_decoder().to(lowercase   ).eval()
						# first forward pass
						A_						:							Tuple      = model(lowercase						,      use_cache=lowercase   )
						A_						:							Optional[int]      = model(lowercase   )
						A_						:							Dict      = model(lowercase						,      use_cache=lowercase   )

						self.parent.assertTrue(len(lowercase   ) == len(lowercase   )   )
						self.parent.assertTrue(len(lowercase   ) == len(lowercase   ) + 1   )

						A_  ,     A_						:							Optional[int]      = outputs.to_tuple()

						# create hypothetical next token and extent to next_input_ids
						A_						:							str      = ids_tensor((self.batch_size, 1)						,      config.vocab_size   )

						# append to next input_ids and
						A_						:							List[Any]      = torch.cat([input_ids, next_tokens]						,      dim=-1   )

						A_						:							Optional[int]      = model(lowercase   )['last_hidden_state']
						A_						:							str      = model(lowercase						,      past_key_values=lowercase   )['last_hidden_state']

						# select random slice
						A_						:							Optional[int]      = ids_tensor((1,)						,      output_from_past.shape[-1]   ).item()
						A_						:							int      = output_from_no_past[:, -1, random_slice_idx].detach()
						A_						:							str      = output_from_past[:, 0, random_slice_idx].detach()

						# test that outputs are equal for slice
						self.parent.assertTrue(torch.allclose(lowercase						,      lowercase						,      atol=1E-3   )   )







				def     lowerCAmelCase_  (		self						,      lowercase						,      lowercase						,      ):



						"""simple docstring"""





						A_						:							List[str]      = UMTaModel(config=lowercase   ).to(lowercase   ).half().eval()
						A_						:							Tuple      = model(**lowercase   )['last_hidden_state']
						self.parent.assertFalse(torch.isnan(lowercase   ).any().item()   )





@require_torch
class UpperCAmelCase		(   __A   ,			__A   ,			__A   ,			unittest.TestCase    ):


				'''simple docstring'''






				lowerCamelCase_        =   (
				    (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
				)
				lowerCamelCase_        =   (UMTaForConditionalGeneration,) if is_torch_available() else ()
				lowerCamelCase_        =   (
				    {
				        '''conversational''': UMTaForConditionalGeneration,
				        '''feature-extraction''': UMTaModel,
				        '''summarization''': UMTaForConditionalGeneration,
				        '''text2text-generation''': UMTaForConditionalGeneration,
				        '''translation''': UMTaForConditionalGeneration,
				        '''question-answering''': UMTaForQuestionAnswering,
				    }
				    if is_torch_available()
				    else {}
				)
				lowerCamelCase_        =   True
				lowerCamelCase_        =   False
				lowerCamelCase_        =   False
				lowerCamelCase_        =   True
				lowerCamelCase_        =   True
				# The small UMT5 model needs higher percentages for CPU/MP tests
				lowerCamelCase_        =   [0.8, 0.9]
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						A_						:							List[Any]      = UMTaModelTester(self   )
				@unittest.skip('Test has a segmentation fault on torch 1.8.0'   )
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						A_						:							Dict      = self.model_tester.prepare_config_and_inputs()
						A_						:							str      = UMTaModel(config_and_inputs[0]   ).to(lowercase   )
						with tempfile.TemporaryDirectory() as tmpdirname:
								torch.onnx.export(
								    lowercase						,      (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])						,      F'''{tmpdirname}/t5_test.onnx'''						,      export_params=lowercase						,      opset_version=9						,      input_names=['input_ids', 'decoder_input_ids']						,      )
				@unittest.skipIf(torch_device == 'cpu'						,      'Cant do half precision'   )
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						A_						:							Any      = self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model_fpaa_forward(*lowercase   )
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						A_						:							Any      = ['encoder_attentions', 'decoder_attentions', 'cross_attentions']
						A_						:							Any      = self.model_tester.prepare_config_and_inputs()
						A_						:							List[Any]      = config_and_inputs[0]
						A_						:							Optional[int]      = UMTaForConditionalGeneration(lowercase   ).eval()
						model.to(lowercase   )

						A_						:							Tuple      = {
						    'head_mask': torch.zeros(config.num_layers						,      config.num_heads						,      device=lowercase   ),
						    'decoder_head_mask': torch.zeros(config.num_decoder_layers						,      config.num_heads						,      device=lowercase   ),
						    'cross_attn_head_mask': torch.zeros(config.num_decoder_layers						,      config.num_heads						,      device=lowercase   ),
						}

						for attn_name, (name, mask) in zip(lowercase						,      head_masking.items()   ):
								A_						:							List[str]      = {name: mask}
								# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
								if name == "head_mask":
										A_						:							Dict      = torch.ones(
										    config.num_decoder_layers						,      config.num_heads						,      device=lowercase   )

								A_						:							Optional[int]      = model.generate(
								    config_and_inputs[1]['input_ids']						,      num_beams=1						,      max_length=3						,      output_attentions=lowercase						,      return_dict_in_generate=lowercase						,      **lowercase						,      )
								# We check the state of decoder_attentions and cross_attentions just from the last step
								A_						:							Tuple      = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
								self.assertEqual(sum([w.sum().item() for w in attn_weights]   )						,      0.0   )







				@unittest.skip('Does not work on the tiny model as we keep hitting edge cases.'   )
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						pass





@require_torch
@require_sentencepiece
@require_tokenizers
class UpperCAmelCase		(   unittest.TestCase    ):


				'''simple docstring'''






				@slow
				@unittest.skip(
				    'Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged'   )
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						A_						:							List[Any]      = UMTaForConditionalGeneration.from_pretrained('google/umt5-small'						,      return_dict=lowercase   ).to(lowercase   )
						A_						:							Optional[Any]      = AutoTokenizer.from_pretrained('google/umt5-small'						,      use_fast=lowercase						,      legacy=lowercase   )
						A_						:							Optional[int]      = [
						    'Bonjour monsieur <extra_id_0> bien <extra_id_1>.',
						    'No se como puedo <extra_id_0>.',
						    'This is the reason why we <extra_id_0> them.',
						    'The <extra_id_0> walks in <extra_id_1>, seats',
						    'A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.',
						]
						A_						:							str      = tokenizer(lowercase						,      return_tensors='pt'						,      padding=lowercase   ).input_ids
						# fmt: off
						A_						:							Union[str, Any]      = torch.tensor(
						    [
						        [ 3_8_5_3_0, 2_1_0_7_0_3, 2_5_6_2_9_9, 1_4_1_0, 2_5_6_2_9_8, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
						        [   8_2_6, 3_2_1, 6_7_1, 2_5_9_2_2, 2_5_6_2_9_9, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
						        [  1_4_6_0, 3_3_9, 3_1_2, 1_9_0_1_4, 1_0_6_2_0, 7_5_8, 2_5_6_2_9_9, 2_3_5_5,2_7_4, 1, 0, 0, 0, 0, 0, 0,0, 0],
						        [   5_1_7, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 3_0_1, 2_5_6_2_9_8, 2_7_5, 1_1_9_9_8_3,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
						        [   3_2_0, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 2_2_3_4, 2_8_9, 2_2_7_5, 3_3_3,6_1_3_9_1, 2_8_9, 2_5_6_2_9_8, 5_4_3, 2_5_6_2_9_7, 1_6_8_7_1_4, 3_2_9, 2_5_6_2_9_6,2_7_4, 1],
						    ]   )
						# fmt: on
						torch.testing.assert_allclose(lowercase						,      lowercase   )

						A_						:							Union[str, Any]      = model.generate(input_ids.to(lowercase   )   )
						A_						:							List[Any]      = [
						    '<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>',
						    '<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
						    '<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
						    '<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
						    '<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
						]
						A_						:							Union[str, Any]      = tokenizer.batch_decode(lowercase   )
						self.assertEqual(lowercase						,      lowercase   )







from __future__ import annotations




def 							UpperCamelCase		(			__lowercase		:					list[int]      ):
		'''simple docstring'''




		A_						:							int      = len(__lowercase      ) // 2

		# choose the middle 3 elements
		A_						:							List[Any]      = lst[m - 1 : m + 2]

		# if middle element is peak
		if three[1] > three[0] and three[1] > three[2]:
				return three[1]

		# if increasing, recurse on right
		elif three[0] < three[2]:
				if len(lst[:m]      ) == 2:
						m -= 1
				return peak(lst[m:]      )

		# decreasing
		else:
				if len(lst[:m]      ) == 2:
						m += 1
				return peak(lst[:m]      )


if __name__ == "__main__":
	import doctest

	doctest.testmod()






